The service and content personalization economy is growing at a staggering pace. Global total internet advertising revenue alone is forecast to grow from USD 135.42 bn in 2014 to USD 239.87 bn in 2019, with an annual growth rate of 12.1% over that period. It is projected to exceed TV advertisement to become the largest single advertising category by the year 2019. Even though network operators provide the infrastructure on which the companies in the personalization domain run their business, most operators are not attempting to partake in this business model.
The earliest work relating to the present invention includes personalized search and recommendation systems. In order to tailor search results to different users, search engine companies, such as GOOGLE and YANDEX, used their search logs containing user browsing behavior to predict the interests of the different users. Similarly, electronic commerce enterprises, such as AMAZON, used shopping histories of different consumers to infer interests of the different consumers for further recommendation. Typically, both personalized search and recommendation systems such as these require specific user input (e.g., user profile and shopping history for product recommendation, browsing/search history for personalization search).
Various publications describe work relating to categorization and on-line advertising. Toubiana, et al., “Adnostic: Privacy Preserving Targeted Advertising,” NDSS 2010 describe a tool that is designed for personalized web advertising with the concern of privacy preservation. The domain-related categorization of Adnostic simply depends on the cosine similarity between Google Ads Preferences categories, names and tags of the concerned web page. The focus of Adnostic is privacy preservation through exploring user profiles/interests and then reporting which ad was viewed without revealing this to the broker. Similarly, with the concern of privacy, Kazienko, et al., “AdROSA-adaptive personalization of web advertising,” Information Sciences, 177(11):2269-2295, 2007 address the problem of web banner advertisements personalization with respect to user privacy wherein none of the user's personal information are stored locally. It is based on extracting knowledge from the web page content and historical user sessions as well as the current behavior of the on-line user, using data-mining techniques. Heer, et al., “Separating the swarm; categorization methods for user sessions on the web,” CHI 2002 propose a categorization method using a clustering algorithm which aims to increase processing efficiency by simply using the features of user view and visit paths without considering web page content. While this could improve the efficiency to some extent, it certainly would lead to information loss.
U.S. Patent Application Publication No. 2010/0082435 describes a customizable ad marker and U.S. Pat. No. 8,521,892 describes a method and apparatus for controlling web page advertisement through incentives and restrictions. The systems described here only consider requests for one particular webpage (URL) and do not learn a machine learning embedding model based on request traces. Further, the systems do not have any capabilities for querying a embedding model learned from fixed-size sequences of request. Moreover, the systems are focused solely on advertisements of a single user, whereas, in contrast, embodiments of the present invention focus on service personalization based on request histories by several users. In further contrast, embodiments of the present invention also apply to various types of network traffic and are not restricted to only webpage request traffic, or in other words, HTTP traffic as specified in the prior art systems.
In sum, none of the existing approaches have addressed the problem by a comprehensive in-network system based on learning privacy-protecting embedding models from request sequences.